


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A   =			logging.get_logger(__name__)





def __UpperCAmelCase							(		__A			,    __A=False			,    __A=False      )    ->       List[Any]:



       '''simple docstring'''





       UpperCAmelCase__					    =  "backbone." if is_semantic else ""

       UpperCAmelCase__					    =  []
       for i in range(config.num_hidden_layers      ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")      )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")      )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")      )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")      )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               (F"""{prefix}cls_token""", "beit.embeddings.cls_token"),
               (F"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
               (F"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
               (F"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
           ]      )

       if has_lm_head:
              # mask token + layernorm
              rename_keys.extend(
                  [
                      ("mask_token", "beit.embeddings.mask_token"),
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                  ]      )
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ("fc_norm.weight", "beit.pooler.layernorm.weight"),
                      ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                      ("head.weight", "classifier.weight"),
                      ("head.bias", "classifier.bias"),
                  ]      )

       return rename_keys





def __UpperCAmelCase							(		__A			,    __A			,    __A=False			,    __A=False      )    ->       List[Any]:



       '''simple docstring'''





       for i in range(config.num_hidden_layers      ):
              UpperCAmelCase__					    =  "backbone." if is_semantic else ""
              # queries, keys and values
              UpperCAmelCase__					    =  state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""      )
              UpperCAmelCase__					    =  state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""      )
              UpperCAmelCase__					    =  state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""      )

              UpperCAmelCase__					    =  in_proj_weight[
                  : config.hidden_size, :
              ]
              UpperCAmelCase__					    =  q_bias
              UpperCAmelCase__					    =  in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              UpperCAmelCase__					    =  in_proj_weight[
                  -config.hidden_size :, :
              ]
              UpperCAmelCase__					    =  v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              UpperCAmelCase__					    =  state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""      )
              UpperCAmelCase__					    =  state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""      )

              UpperCAmelCase__					    =  gamma_a
              UpperCAmelCase__					    =  gamma_a





def __UpperCAmelCase							(		__A			,    __A			,    __A      )    ->       Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__					    =  dct.pop(a__      )
       UpperCAmelCase__					    =  val





def __UpperCAmelCase							(		)    ->       Any:



       '''simple docstring'''





       UpperCAmelCase__					    =  "http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCAmelCase__					    =  Image.open(requests.get(a__			,    stream=a__      ).raw      )
       return im





@torch.no_grad()
def __UpperCAmelCase							(		__A			,    __A			,    __A=False      )    ->       Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__					    =  False if "rvlcdip" in checkpoint_url else True
       UpperCAmelCase__					    =  BeitConfig(use_absolute_position_embeddings=a__			,    use_mask_token=a__      )

       # size of the architecture
       if "large" in checkpoint_url or "dit-l" in checkpoint_url:
              UpperCAmelCase__					    =  1_0_2_4
              UpperCAmelCase__					    =  4_0_9_6
              UpperCAmelCase__					    =  2_4
              UpperCAmelCase__					    =  1_6

       # labels
       if "rvlcdip" in checkpoint_url:
              UpperCAmelCase__					    =  1_6
              UpperCAmelCase__					    =  "huggingface/label-files"
              UpperCAmelCase__					    =  "rvlcdip-id2label.json"
              UpperCAmelCase__					    =  json.load(open(hf_hub_download(a__			,    a__			,    repo_type="dataset"      )			,    "r"      )      )
              UpperCAmelCase__					    =  {int(a__      ): v for k, v in idalabel.items()}
              UpperCAmelCase__					    =  idalabel
              UpperCAmelCase__					    =  {v: k for k, v in idalabel.items()}

       # load state_dict of original model, remove and rename some keys
       UpperCAmelCase__					    =  torch.hub.load_state_dict_from_url(a__			,    map_location="cpu"      )["model"]

       UpperCAmelCase__					    =  create_rename_keys(a__			,    has_lm_head=a__      )
       for src, dest in rename_keys:
              rename_key(a__			,    a__			,    a__      )
       read_in_q_k_v(a__			,    a__			,    has_lm_head=a__      )

       # load HuggingFace model
       UpperCAmelCase__					    =  BeitForMaskedImageModeling(a__      ) if has_lm_head else BeitForImageClassification(a__      )
       model.eval()
       model.load_state_dict(a__      )

       # Check outputs on an image
       UpperCAmelCase__					    =  BeitImageProcessor(
           size=config.image_size			,    resample=PILImageResampling.BILINEAR			,    do_center_crop=a__      )
       UpperCAmelCase__					    =  prepare_img()

       UpperCAmelCase__					    =  image_processor(images=a__			,    return_tensors="pt"      )
       UpperCAmelCase__					    =  encoding["pixel_values"]

       UpperCAmelCase__					    =  model(a__      )
       UpperCAmelCase__					    =  outputs.logits

       # verify logits
       UpperCAmelCase__					    =  [1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
       assert logits.shape == torch.Size(a__      ), "Shape of logits not as expected"

       Path(a__      ).mkdir(exist_ok=a__      )
       print(F"""Saving model to {pytorch_dump_folder_path}"""      )
       model.save_pretrained(a__      )
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
       image_processor.save_pretrained(a__      )

       if push_to_hub:
              if has_lm_head:
                     UpperCAmelCase__					    =  "dit-base" if "base" in checkpoint_url else "dit-large"
              else:
                     UpperCAmelCase__					    =  "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
              image_processor.push_to_hub(
                  repo_path_or_name=Path(a__			,    a__      )			,    organization="nielsr"			,    commit_message="Add image processor"			,    use_temp_dir=a__			,    )
              model.push_to_hub(
                  repo_path_or_name=Path(a__			,    a__      )			,    organization="nielsr"			,    commit_message="Add model"			,    use_temp_dir=a__			,    )


if __name__ == "__main__":
    A   =			argparse.ArgumentParser()

    parser.add_argument(
        "--checkpoint_url",
        default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
        type=str,
        help="URL to the original PyTorch checkpoint (.pth file).",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
    )
    A   =			parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					DDIMPipeline
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __A			       =					PipelineTesterMixin.required_optional_params - {
     '''num_images_per_prompt''',
     '''latents''',
     '''callback''',
     '''callback_steps''',
 }
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=("DownBlock2D", "AttnDownBlock2D")							, up_block_types=("AttnUpBlock2D", "UpBlock2D")							, )
   _UpperCamelCase           =    DDIMScheduler()
   _UpperCamelCase           =    {"unet": unet, "scheduler": scheduler}
   return components







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   List[str]							, lowercase_				:   Optional[Any]=0)						->	Optional[Any]:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase           =    torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
   _UpperCamelCase           =    {
       "batch_size": 1,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"

   _UpperCamelCase           =    self.get_dummy_components()
   _UpperCamelCase           =    self.pipeline_class(**lowercase_)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    self.get_dummy_inputs(lowercase_)
   _UpperCamelCase           =    pipe(**lowercase_).images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   self.assertEqual(image.shape							, (1, 32, 32, 3))
   _UpperCamelCase           =    np.array(
       [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4])
   _UpperCamelCase           =    np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(lowercase_							, 1e-3)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Union[str, Any]:


   """simple docstring"""







   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_save_load_local(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   super().test_save_load_optional_components(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   super().test_inference_batch_single_identical(expected_max_diff=3e-3)




@slow
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-cifar10-32"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler()

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddim.to(lowercase_)
   ddim.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddim(generator=lowercase_							, eta=0.0							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _UpperCamelCase           =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-ema-bedroom-256"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler.from_pretrained(lowercase_)

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddpm.to(lowercase_)
   ddpm.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddpm(generator=lowercase_							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 256, 256, 3)
   _UpperCamelCase           =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




